We welcome contributions or suggestions from other developers. Please contact us if you have questions or would like to discuss an addition or major modifications to the Giotto main code. The source code for Giotto Suite may be found on our GitHub repository.
The Giotto packages exist at the drieslab repository on GitHub. Some guidelines for pull requests (PRs) are the following:
Edits to code should start on a new and purpose-made branch based on the packages’s dev branch (one of the following).
drieslab/Giotto@suite_dev
drieslab/GiottoVisuals@dev
drieslab/GiottoClass@dev
drieslab/GiottoUtils@dev
PRs when ready, should then be made to dev branches of the Giotto modules.
PRs will be reviewed by a core dev member after which a decision is made if it can be directly added to dev or if there are further revisions that are desired.
Following a particular programming style will help programmers read and understand source code conforming to the style, and help to avoid introducing errors. Here we present a small list of guidelines on what is considered a good practice when writing R code in Giotto package. Most of them are adapted from Bioconductor - coding style or Google’s R Style Guide. These guidelines are preferences and strongly encouraged!
Overall style
We follow the BioConductor styling. You can set this up easily by installing biocthis and styler.
# package installations
BiocManager::install("biocthis")
install.packages("styler")
# styling a file
b_style <- biocthis::bioc_style()
styler::style_file(path = "[???]", transformers = b_style)
# styling the active package (may lead to lots of conflicts)
# !! This should only be done be core devs with a lot of caution and forewarning !!
styler::style_pkg(transformers = b_style)setting your default indent size to be 4 spaces instead of 2 is also recommended.
Function types
exported - Core functionality for users to directly use. These should have clear names and documentation
exported utility - Secondary functionalities that are helpful to also have available, but are not directly related to data processing, analysis, and visualization. Examples are dt_to_matrix() or wrap_msg()
internal - Functions that are never intended to be used outside of a module package. These are functions only relevant to the internals of one package, for example .detect_in_dir() from Giotto’s internals which is pretty nondescript and mainly there to help with code organization.
Naming
Use camelCase for exported functions. ex: functionName()
Use snake_case for exported utiliity functions. ex: function_name()
Use . prefix AND snake_case for internal functions. ex: .function_name()
Use snake_case for parameter/argument names.
Never use . as a separator in function naming. (in the S3 class system, fun(x) where x is class foo will dispatch to fun.foo())
Use of symbols Do not use any non-UTF-8 characters unless provided as the escape code. For example: \u00F6 for ö Beyond these guidelines, styler should be used in order to maintain code uniformity.
Most Giotto commands can accept several matrix classes (DelayedMatrix, SparseM, Matrix or base matrix). To facilitate this we provide flexible wrappers that work on any type of matrix class.
mean_flex(): analogous to mean()
rowSums_flex(): analogous to rowSums()
rowMeans_flex(): analogous to rowMeans()
colSums_flex(): analogous to colSums()
colMeans_flex(): analogous to colMeans()
cor_flex(): analogous to cor()
Giotto has a number of auxiliary or convenience functions that might help you to adapt your code or write new code for Giotto. We encourage you to use these small functions to maintain uniformity throughout the code.
lapply_flex(): analogous to lapply() and works for both windows and unix systems
all_plots_save_function(): compatible with Giotto instructions and helps to automatically save generated plots
plot_output_handler(): further wraps all_plots_save_function and includes handling for return_plot and show_plot and Giotto instructions checking
determine_cores(): determine the number of cores to use if a user does not set this explicitly
get_os(): identify the operating system
update_giotto_params(): will catch and store the parameters for each used command on a giotto object
wrap_txt(), wrap_msg(), etc: text and message formatting functions
vmsg(): framework for Giotto’s verbosity-flagged messages
package_check(): to check if a package exists, works for packages on CRAN, Bioconductor and Github
Should be used within your contribution code if it requires the use of packages not in Giotto’s DESCRIPTION file’s depends imports section.
Has the additional benefit that it will suggest to the user how to download the package if it is not available. To keep the size of Giotto within limits we prefer not to add too many new dependencies.
Giotto tracks packages and functions to import in a centralized file. When adding code that requires functions from another package, add the roxygen tags to the package_imports.R file for that Giotto module.
Giotto stores information in different slots, which can be accessed through these getters and setters functions. They can be found in the accessors.R file.
setGiotto(): Sets any Giotto subobject
getCellMetadata(): Gets cell metadata
setCellMetadata(): Sets cell metadata
getFeatureMetadata(): Gets feature metadata
getFeatureMetadata(): Sets feature metadata
getExpression(): To select the expression matrix to use
setExpression(): Sets a new expression matrix to the expression slot
getSpatialLocations(): Get spatial locations to use
setSpatialLocations(): Sets new spatial locations
getDimReduction(): To select the dimension reduction values to use
setDimReduction(): Sets new dimension reduction object
getNearestNetwork(): To select the nearest neighbor network (kNN or sNN) to use
setNearestNetwork(): Sets a new nearest neighbor network (kNN or sNN)
getSpatialNetwork(): To select the spatial network to use
setSpatialNetwork(): Sets a new spatial network
getPolygonInfo(): Gets spatial polygon information
setPolygonInfo(): Set new spatial polygon information
getFeatureInfo(): Gets spatial feature information
setFeatureInfo(): Sets new spatial feature information
getSpatialEnrichment(): Gets spatial enrichment information
setSpatialEnrichment(): Sets new spatial enrichment information
getMultiomics(): Gets multiomics information
setMultiomics(): Sets multiomics information
To use Python code we prefer to create a python wrapper/functions around the python code, which can then be sourced by reticulate. As an example we show the basic principles of how we implemented the Leiden clustering algorithm.
python_leiden.py in /inst/python:import igraph as ig
import leidenalg as la
import pandas as pd
import networkx as nx
def python_leiden(df, partition_type, initial_membership=None, weights=None, n_iterations=2, seed=None, resolution_parameter = 1):
# create networkx object
Gx = nx.from_pandas_edgelist(df = df, source = 'from', target = 'to', edge_attr = 'weight')
# get weight attribute
myweights = nx.get_edge_attributes(Gx, 'weight')
....
return(leiden_dfr)python_leiden_function = system.file("python", "python_leiden.py", package = 'Giotto') reticulate::source_python(file = python_leiden_function)doLeidenCLuster() for more detailed information.pyth_leid_result = python_leiden(
df = network_edge_dt,
partition_type = partition_type,
initial_membership = init_membership,
weights = 'weight',
n_iterations = n_iterations,
seed = seed_number,
resolution_parameter = resolution
)